{
"classes_names": [
"tench",
"goldfish",
"great_white_shark",
"tiger_shark",
"hammerhead",
"electric_ray",
"stingray",
"cock",
"hen",
"ostrich",
"brambling",
"goldfinch",
"house_finch",
"junco",
"indigo_bunting",
"robin",
"bulbul",
"jay",
"magpie",
"chickadee",
"water_ouzel",
"kite",
"bald_eagle",
"vulture",
"great_grey_owl",
"European_fire_salamander",
"common_newt",
"eft",
"spotted_salamander",
"axolotl",
"bullfrog",
"tree_frog",
"tailed_frog",
"loggerhead",
"leatherback_turtle",
"mud_turtle",
"terrapin",
"box_turtle",
"banded_gecko",
"common_iguana",
"American_chameleon",
"whiptail",
"agama",
"frilled_lizard",
"alligator_lizard",
"Gila_monster",
"green_lizard",
"African_chameleon",
"Komodo_dragon",
"African_crocodile",
"American_alligator",
"triceratops",
"thunder_snake",
"ringneck_snake",
"hognose_snake",
"green_snake",
"king_snake",
"garter_snake",
"water_snake",
"vine_snake",
"night_snake",
"boa_constrictor",
"rock_python",
"Indian_cobra",
"green_mamba",
"sea_snake",
"horned_viper",
"diamondback",
"sidewinder",
"trilobite",
"harvestman",
"scorpion",
"black_and_gold_garden_spider",
"barn_spider",
"garden_spider",
"black_widow",
"tarantula",
"wolf_spider",
"tick",
"centipede",
"black_grouse",
"ptarmigan",
"ruffed_grouse",
"prairie_chicken",
"peacock",
"quail",
"partridge",
"African_grey",
"macaw",
"sulphur-crested_cockatoo",
"lorikeet",
"coucal",
"bee_eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted_merganser",
"goose",
"black_swan",
"tusker",
"echidna",
"platypus",
"wallaby",
"koala",
"wombat",
"jellyfish",
"sea_anemone",
"brain_coral",
"flatworm",
"nematode",
"conch",
"snail",
"slug",
"sea_slug",
"chiton",
"chambered_nautilus",
"Dungeness_crab",
"rock_crab",
"fiddler_crab",
"king_crab",
"American_lobster",
"spiny_lobster",
"crayfish",
"hermit_crab",
"isopod",
"white_stork",
"black_stork",
"spoonbill",
"flamingo",
"little_blue_heron",
"American_egret",
"bittern",
"crane",
"limpkin",
"European_gallinule",
"American_coot",
"bustard",
"ruddy_turnstone",
"red-backed_sandpiper",
"redshank",
"dowitcher",
"oystercatcher",
"pelican",
"king_penguin",
"albatross",
"grey_whale",
"killer_whale",
"dugong",
"sea_lion",
"Chihuahua",
"Japanese_spaniel",
"Maltese_dog",
"Pekinese",
"Shih-Tzu",
"Blenheim_spaniel",
"papillon",
"toy_terrier",
"Rhodesian_ridgeback",
"Afghan_hound",
"basset",
"beagle",
"bloodhound",
"bluetick",
"black-and-tan_coonhound",
"Walker_hound",
"English_foxhound",
"redbone",
"borzoi",
"Irish_wolfhound",
"Italian_greyhound",
"whippet",
"Ibizan_hound",
"Norwegian_elkhound",
"otterhound",
"Saluki",
"Scottish_deerhound",
"Weimaraner",
"Staffordshire_bullterrier",
"American_Staffordshire_terrier",
"Bedlington_terrier",
"Border_terrier",
"Kerry_blue_terrier",
"Irish_terrier",
"Norfolk_terrier",
"Norwich_terrier",
"Yorkshire_terrier",
"wire-haired_fox_terrier",
"Lakeland_terrier",
"Sealyham_terrier",
"Airedale",
"cairn",
"Australian_terrier",
"Dandie_Dinmont",
"Boston_bull",
"miniature_schnauzer",
"giant_schnauzer",
"standard_schnauzer",
"Scotch_terrier",
"Tibetan_terrier",
"silky_terrier",
"soft-coated_wheaten_terrier",
"West_Highland_white_terrier",
"Lhasa",
"flat-coated_retriever",
"curly-coated_retriever",
"golden_retriever",
"Labrador_retriever",
"Chesapeake_Bay_retriever",
"German_short-haired_pointer",
"vizsla",
"English_setter",
"Irish_setter",
"Gordon_setter",
"Brittany_spaniel",
"clumber",
"English_springer",
"Welsh_springer_spaniel",
"cocker_spaniel",
"Sussex_spaniel",
"Irish_water_spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"Old_English_sheepdog",
"Shetland_sheepdog",
"collie",
"Border_collie",
"Bouvier_des_Flandres",
"Rottweiler",
"German_shepherd",
"Doberman",
"miniature_pinscher",
"Greater_Swiss_Mountain_dog",
"Bernese_mountain_dog",
"Appenzeller",
"EntleBucher",
"boxer",
"bull_mastiff",
"Tibetan_mastiff",
"French_bulldog",
"Great_Dane",
"Saint_Bernard",
"Eskimo_dog",
"malamute",
"Siberian_husky",
"dalmatian",
"affenpinscher",
"basenji",
"pug",
"Leonberg",
"Newfoundland",
"Great_Pyrenees",
"Samoyed",
"Pomeranian",
"chow",
"keeshond",
"Brabancon_griffon",
"Pembroke",
"Cardigan",
"toy_poodle",
"miniature_poodle",
"standard_poodle",
"Mexican_hairless",
"timber_wolf",
"white_wolf",
"red_wolf",
"coyote",
"dingo",
"dhole",
"African_hunting_dog",
"hyena",
"red_fox",
"kit_fox",
"Arctic_fox",
"grey_fox",
"tabby",
"tiger_cat",
"Persian_cat",
"Siamese_cat",
"Egyptian_cat",
"cougar",
"lynx",
"leopard",
"snow_leopard",
"jaguar",
"lion",
"tiger",
"cheetah",
"brown_bear",
"American_black_bear",
"ice_bear",
"sloth_bear",
"mongoose",
"meerkat",
"tiger_beetle",
"ladybug",
"ground_beetle",
"long-horned_beetle",
"leaf_beetle",
"dung_beetle",
"rhinoceros_beetle",
"weevil",
"fly",
"bee",
"ant",
"grasshopper",
"cricket",
"walking_stick",
"cockroach",
"mantis",
"cicada",
"leafhopper",
"lacewing",
"dragonfly",
"damselfly",
"admiral",
"ringlet",
"monarch",
"cabbage_butterfly",
"sulphur_butterfly",
"lycaenid",
"starfish",
"sea_urchin",
"sea_cucumber",
"woo
megengine框架的图像分类VGG13模型(ImageNet)
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更新于2022-10-17
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**正文**
在深度学习领域,图像分类是一项基础且重要的任务,用于识别图像中物体的类别。MegEngine 是一个高效的开源深度学习框架,它提供了一系列预训练的模型,以支持快速开发和研究。在这个场景中,我们关注的是 MegEngine 框架中的 VGG13 模型,该模型是针对 ImageNet 数据集进行训练的。ImageNet 是一个大规模的视觉数据库,包含超过一千万张标注了类别信息的高分辨率图像,用于图像分类和物体检测的挑战。
VGG13 模型由英国牛津大学的 Visual Geometry Group(VGG)提出,因其网络结构深而得名,包含13个卷积层,随后是几个全连接层。这种深度结构使得 VGG13 在2014年的 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 中表现突出,为后续的深度学习模型设计奠定了基础。
在 MegEngine 中实现 VGG13,通常会采用批量归一化(Batch Normalization, BN)技术,这是为了加速训练过程,减少内部协变量位移,并提高模型的泛化能力。因此,我们看到的两个模型版本:`ctu_params_vgg13_bn.json` 和 `vgg13_bn.pkl`,都带有 `_bn` 后缀,表示它们采用了批量归一化层。
批量归一化层在每个卷积或全连接层后插入,它对输入数据进行标准化处理,使得每一层的输入保持恒定的均值和方差。这有助于模型在训练初期更快地收敛,同时可以稍微降低模型对初始化参数的敏感性。
另一方面,`ctu_params_vgg13.json` 和 `vgg13.pkl` 文件则可能是没有使用批量归一化的 VGG13 模型版本。在训练过程中,没有 BN 层的模型可能会需要更长的训练时间,但有时在某些任务上可能获得更好的性能。
`.json` 文件通常存储模型的超参数,如学习率、权重衰减等,而 `.pkl` 文件则保存了模型的权重和偏置,这些是训练过程中学到的数值。使用这些权重文件,我们可以直接在 MegEngine 中加载预训练的 VGG13 模型,进行图像分类任务,或者用作其他深度学习任务的特征提取器。
在实际应用中,我们可以通过以下步骤来利用这些模型:
1. 安装 MegEngine 库。
2. 加载 `.pkl` 文件中的模型权重。
3. 构建与预训练模型匹配的网络结构。
4. 使用 MegEngine 的 `Graph` API 进行前向传播,处理新的输入图像。
5. 输出分类结果。
MegEngine 的 VGG13 模型为开发者提供了一种强大的工具,用于在 ImageNet 数据集上执行图像分类。无论是选择带批量归一化的版本还是不带的,都取决于具体应用场景的需求和优化目标。通过理解模型的工作原理以及如何在 MegEngine 中使用这些预训练权重,我们可以更好地利用深度学习技术解决实际问题。
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